Most unsupervised disentanglement approaches are built upon Variational AutoEncoders (VAEs) [1]}, which are a type of generative model trained with a reconstruction and regularization loss. The regularization pushes the posterior distribution of the generative factors toward an isotropic Gaussian, encouraging its representations toward a factorized prior with independent dimensions, under the hope that this independence is close to the desired semantic disentanglement. However, because the reconstruction loss limits the amount of disentanglement that can be imposed in a vanilla VAE, these approaches have largely focused on augmenting the regularization loss to increase disentanglement without degrading reconstruction quality [2]}, [3]}. Generative Adversarial Networks (GANs) [4]} have also been used for disentanglement: the generator inputs are divided into latent and noise components, and a regularization loss is added to maximize the mutual information (MI) between the observations and latent components to the discriminator loss [5]}, [6]}.
